AI-900: Azure AI Fundamentals Practice Exam & Simulated PBQs

Prepare for the AI-900: Microsoft Azure AI Fundamentals exam with this comprehensive practice exam. This exam is designed to help you assess your knowledge and readiness for the actual certification test. With a focus on AI concepts and services in Microsoft Azure, this practice exam covers a wide range of topics including machine learning, natural language processing, computer vision, and more. By taking this practice exam, you can familiarize yourself with the format of the actual test and identify areas where you may need to further study and prepare.

AI-900: Microsoft Azure AI Fundamentals Practice Exam includes a set of questions that closely mirror the format and content of the real certification exam. This allows you to simulate the testing environment and gain valuable experience in answering questions within the time constraints of the actual test. The practice exam also provides detailed explanations for each question, helping you understand the rationale behind the correct answers and reinforcing your understanding of AI concepts and services in Microsoft Azure.

This practice exam as part of your preparation for the AI-900 certification, you can increase your confidence and readiness to tackle the actual test. Whether you are new to AI and machine learning or looking to validate your existing knowledge, this practice exam is a valuable tool for honing your skills and ensuring success on exam day. Take the next step in your AI journey with the AI-900: Microsoft Azure AI Fundamentals Practice Exam.

AI-900: Microsoft Azure AI Fundamentals is a comprehensive and updated course designed to provide learners with a solid understanding of artificial intelligence (AI) concepts and how they are implemented in Microsoft Azure. This course covers a wide range of topics, including machine learning, computer vision, natural language processing, and conversational AI. With the latest syllabus updates, learners can expect to gain knowledge about the latest AI technologies and tools available in Microsoft Azure, ensuring they are well-equipped to tackle real-world AI challenges.

Throughout the course, students will have the opportunity to explore various AI services offered by Microsoft Azure, such as Azure Cognitive Services, Azure Machine Learning, and Azure Bot Services. They will also learn how to leverage these services to build AI solutions that can analyze images, interpret speech, understand natural language, and engage in intelligent conversations. Additionally, the course will cover important ethical considerations related to AI, ensuring that learners understand the responsible use of AI technologies.

AI-900 : Microsoft Azure AI Fundamentals Exam details :

• Exam Name: Microsoft Certified – Azure AI Fundamentals

• Exam Code: AI-900

• Exam Price:$99 (USD)

• Number of Questions: Maximum of 40-60 questions,

• Type of Questions: Multiple Choice Questions (single and multiple response), drag and drops and performance-based,

• Length of Test: 60 Minutes. The exam is available in English and Japanese languages.

• Passing Score: 700 / 1000

• Languages : English, Japanese, Korean, and Simplified Chinese

• Schedule Exam : Pearson VUE

AI-900 : Microsoft Azure AI Fundamentals Certification Exams skill questions:

Skill Measurement Exam Topics:–

• Describe Artificial Intelligence workloads and considerations (15–20%)

• Describe fundamental principles of machine learning on Azure (20–25%)

• Describe features of computer vision workloads on Azure (15–20%)

• Describe features of Natural Language Processing (NLP) workloads on Azure (15–20%)

• Describe features of generative AI workloads on Azure (15–20%)

Describe Artificial Intelligence workloads and considerations (15–20%)

Identify features of common AI workloads

• Identify features of content moderation and personalization workloads

• Identify computer vision workloads

• Identify natural language processing workloads

• Identify knowledge mining workloads

• Identify document intelligence workloads

• Identify features of generative AI workloads

Identify guiding principles for responsible AI

• Describe considerations for fairness in an AI solution

• Describe considerations for reliability and safety in an AI solution

• Describe considerations for privacy and security in an AI solution

• Describe considerations for inclusiveness in an AI solution

• Describe considerations for transparency in an AI solution

• Describe considerations for accountability in an AI solution

Describe fundamental principles of machine learning on Azure (20–25%)

Identify common machine learning techniques

• Identify regression machine learning scenarios

• Identify classification machine learning scenarios

• Identify clustering machine learning scenarios

• Identify features of deep learning techniques

Describe core machine learning concepts

• Identify features and labels in a dataset for machine learning

• Describe how training and validation datasets are used in machine learning

Describe Azure Machine Learning capabilities

• Describe capabilities of automated machine learning

• Describe data and compute services for data science and machine learning

• Describe model management and deployment capabilities in Azure Machine Learning

Describe features of computer vision workloads on Azure (15–20%)

Identify common types of computer vision solution

• Identify features of image classification solutions

• Identify features of object detection solutions

• Identify features of optical character recognition solutions

• Identify features of facial detection and facial analysis solutions

Identify Azure tools and services for computer vision tasks

• Describe capabilities of the Azure AI Vision service

• Describe capabilities of the Azure AI Face detection service

Describe features of Natural Language Processing (NLP) workloads on Azure (15–20%)

Identify features of common NLP Workload Scenarios

• Identify features and uses for key phrase extraction

• Identify features and uses for entity recognition

• Identify features and uses for sentiment analysis

• Identify features and uses for language modeling

• Identify features and uses for speech recognition and synthesis

• Identify features and uses for translation

Identify Azure tools and services for NLP workloads

• Describe capabilities of the Azure AI Language service

• Describe capabilities of the Azure AI Speech service

Describe features of generative AI workloads on Azure (15–20%)

Identify features of generative AI solutions

• Identify features of generative AI models

• Identify common scenarios for generative AI

• Identify responsible AI considerations for generative AI

Identify capabilities of Azure OpenAI Service

• Describe natural language generation capabilities of Azure OpenAI Service

• Describe code generation capabilities of Azure OpenAI Service

• Describe image generation capabilities of Azure OpenAI Service

By the end of the AI-900: Microsoft Azure AI Fundamentals course, students will have a strong foundation in AI concepts and practical skills in using AI tools and services within the Microsoft Azure environment. Whether they are new to AI or looking to expand their knowledge and skills, this course provides a valuable learning experience that can benefit individuals pursuing careers in AI development, data science, and cloud computing. With the latest syllabus updates, learners can be confident that they are receiving the most relevant and up-to-date information in the field of AI.

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